GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2502.01113v1
• Date: Mon, 03 Feb 2025 07:04:29 GMT
• Title: GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
• Authors: Linhao Luo, Zicheng Zhao, Gholamreza Haffari, Dinh Phung, Chen Gong, Shirui Pan,
• Abstract summary: We introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation. GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships. It achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws.
• Score: 84.41557981816077
• License:
• Abstract: Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relationships between pieces of knowledge, limiting their performance in intricate reasoning that requires integrating knowledge from multiple sources. Recently, graph-enhanced retrieval augmented generation (GraphRAG) builds graph structure to explicitly model these relationships, enabling more effective and efficient retrievers. Nevertheless, its performance is still hindered by the noise and incompleteness within the graph structure. To address this, we introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation. GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships. The GFM with 8M parameters undergoes a two-stage training process on large-scale datasets, comprising 60 knowledge graphs with over 14M triples and 700k documents. This results in impressive performance and generalizability for GFM-RAG, making it the first graph foundation model applicable to unseen datasets for retrieval without any fine-tuning required. Extensive experiments on three multi-hop QA datasets and seven domain-specific RAG datasets demonstrate that GFM-RAG achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws, highlighting its potential for further improvement.

Related papers

• Graph Foundation Models for Recommendation: A Comprehensive Survey [55.70529188101446]
  Large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted. Recent research has focused on graph foundation models (GFMs) GFMs integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding.
  arXiv  Detail & Related papers  (2025-02-12T12:13:51Z)
• Retrieval-Augmented Generation with Graphs (GraphRAG) [84.29507404866257]
  Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information. Unlike conventional RAG, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains.
  arXiv  Detail & Related papers  (2024-12-31T06:59:35Z)
• LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model [27.047809869136458]
  Graph foundation models (GFMs) have recently gained significant attention. Current research tends to focus on specific subsets of graph learning tasks. We propose GFMBench-a systematic and comprehensive benchmark comprising 26 datasets. We also introduce LangGFM, a novel GFM that relies entirely on large language models.
  arXiv  Detail & Related papers  (2024-10-19T03:27:19Z)
• Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks [2.7523980737007414]
  We propose TADA, an efficient and effective front-mounted data augmentation framework for graph neural networks (GNNs) on high-degree graphs (HDGs) Under the hood, TADA includes two key modules: (i) feature expansion with structure embeddings, and (ii) topology- and attribute-aware graph sparsification. TADA considerably improves the predictive performance of mainstream GNN models on 8 real homophilic/heterophilic HDGs in terms of node classification.
  arXiv  Detail & Related papers  (2024-06-08T14:14:19Z)
• Position: Graph Foundation Models are Already Here [53.737868336014735]
  Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain. We propose a novel perspective for the GFM development by advocating for a graph vocabulary'' This perspective can potentially advance the future GFM design in line with the neural scaling laws.
  arXiv  Detail & Related papers  (2024-02-03T17:24:36Z)
• LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension [13.173307471333619]
  We propose the LUKE-Graph, a model that builds a heterogeneous graph based on the intuitive relationships between entities in a document. We then use the Attention reading (RGAT) to fuse the graph's reasoning information and the contextual representation encoded by the pre-trained LUKE model. Experimental results demonstrate that the LUKE-Graph achieves state-of-the-art performance with commonsense reasoning.
  arXiv  Detail & Related papers  (2023-03-12T14:31:44Z)
• An Empirical Study of Retrieval-enhanced Graph Neural Networks [48.99347386689936]
  Graph Neural Networks (GNNs) are effective tools for graph representation learning. We propose a retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the choice of graph neural network models. We conduct comprehensive experiments over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach substantial improvements over existing GNNs.
  arXiv  Detail & Related papers  (2022-06-01T09:59:09Z)
• Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation [56.73834525802723]
  Lightweight Dynamic Graph Convolutional Networks (LDGCNs) are proposed. LDGCNs capture richer non-local interactions by synthesizing higher order information from the input graphs. We develop two novel parameter saving strategies based on the group graph convolutions and weight tied convolutions to reduce memory usage and model complexity.
  arXiv  Detail & Related papers  (2020-10-09T06:03:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.